train_ppo.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2023 The OpenRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""
import numpy as np
from make_env import make

from examples.envpool.envpool_wrappers import VecAdapter, VecMonitor
from openrl.configs.config import create_config_parser
from openrl.modules.common.ppo_net import PPONet as Net
from openrl.runners.common import PPOAgent as Agent


def train():
    # create the neural network
    cfg_parser = create_config_parser()
    cfg = cfg_parser.parse_args()

    # create environment, set environment parallelism to 9
    env = make(
        "CartPole-v1",
        render_mode=None,
        env_num=9,
        asynchronous=False,
        env_wrappers=[VecAdapter, VecMonitor],
        env_type="gym",
    )

    net = Net(
        env,
        cfg=cfg,
    )
    # initialize the trainer
    agent = Agent(net, use_wandb=False, project_name="CartPole-v1")
    # start training, set total number of training steps to 20000
    agent.train(total_time_steps=20000)

    env.close()
    return agent


def evaluation(agent):
    # begin to test
    # Create an environment for testing and set the number of environments to interact with to 9. Set rendering mode to group_human.
    render_mode = "group_human"
    render_mode = None
    env = make(
        "CartPole-v1",
        env_wrappers=[VecAdapter, VecMonitor],
        render_mode=render_mode,
        env_num=9,
        asynchronous=True,
        env_type="gym",
    )
    # The trained agent sets up the interactive environment it needs.
    agent.set_env(env)
    # Initialize the environment and get initial observations and environmental information.
    obs, info = env.reset()
    done = False
    step = 0
    total_step, total_reward = 0, 0
    while not np.any(done):
        # Based on environmental observation input, predict next action.
        action, _ = agent.act(obs, deterministic=True)
        obs, r, done, info = env.step(action)
        step += 1
        total_step += 1
        total_reward += np.mean(r)
        if step % 50 == 0:
            print(f"{step}: reward:{np.mean(r)}")
    env.close()
    print("total step:", total_step)
    print("total reward:", total_reward)


if __name__ == "__main__":
    agent = train()
    evaluation(agent)